Quadratic regression modelsArtificial\nneural networkArabian Journal for Science and Engineering - Concreting and curing under hot climatic conditions pose adverse effects on the characteristics of concrete. These challenges have prompted cement and......
Concentrating on five public health units (PHU) in the Greater Toronto Area in Canada and with the help of segmented regression12, Dainton and Hay assess the relationship between the effective R-valueRtand each Google mobility variable separately. They and find a more pronounced relationship during...
same expenditure survey. Furthermore, we corrected for endogeneity of the expenditure variable in the model by using the augmented regression technique. Heeb et al. ([2003]) analyzed the effect in Switzerland of a price reduction on spirits in 1999. The reduction was due to a tariff reduction...
Sparse multinomial logistic regressionQuadratic regularization methodGlobal convergenceLocally quadratic convergenceNumerical experimentSparse multinomial logistic regression has recently received widespread attention. It provides a useful tool for solving multi-classification problems in various fields, such as signal...
Regression machineSoft sensorInspired by the tremendous achievements of meta-learning in various fields, this paper proposes the local quadratic embedding learning (LQEL) algorithm for regression problems based on metric learning and neural networks (NNs). First, Mahalanobis metric learning is improved ...
First, a single objective empirical optimization procedure is developed.; Second, using the simulation results, Response Surface Methodology (RSM) and multiple regression analysis are combined to develop and construct a metamodel of the FMS. The metamodel is first subjected to statistical analysis to...
Deep Gaussian process regression for lithium-ion battery health prognosis and degradation mode diagnosis. J. Power Sources 2020, 445, 227281. [Google Scholar] [CrossRef] Hebbal, A.; Brevault, L.; Balesdent, M.; Talbi, E.G.; Melab, N. Bayesian Optimization using Deep Gaussian Processes. ...
Deep Gaussian process regression for lithium-ion battery health prognosis and degradation mode diagnosis. J. Power Sources 2020, 445, 227281. [Google Scholar] [CrossRef] Hebbal, A.; Brevault, L.; Balesdent, M.; Talbi, E.G.; Melab, N. Bayesian Optimization using Deep Gaussian Processes. ...
Deep Gaussian process regression for lithium-ion battery health prognosis and degradation mode diagnosis. J. Power Sources 2020, 445, 227281. [Google Scholar] [CrossRef] Hebbal, A.; Brevault, L.; Balesdent, M.; Talbi, E.G.; Melab, N. Bayesian Optimization using Deep Gaussian Processes. ...